The Tokenization Paradox: How Character Compression Backfires in LLM Inference

When teams attempt to reduce LLM inference costs by deleting characters from prompts, they unknowingly trigger a counter-intuitive tokenizer behavior: costs spike instead of drop. The culprit is Byte Pair Encoding (BPE), which learns to compress well-formed text efficiently but lacks learned merges for fragmented input, forcing fallback to costly single-byte tokens. This production-level insight underscores the critical importance of understanding tokenizer mechanics before optimizing costs.